ICT Express

Vision, Radar, and LiDAR sensors are widely used for autonomous vehicle perception technology. Especially object detection and classification are primarily dependent on vision sensors. However, under poor lighting conditions, dazzling sunlight, or bad weather an object might be difficult to be identified with general vision sensors. In this paper, we propose a sensor fusion system that combines a thermal infrared camera and a LiDAR sensor that can reliably detect and identify objects even in environments with poor visibility, such as day or night. The proposed method obtains the external parameters of the two sensors by designing and manufacturing a 3D calibration target to externally calibrate the thermal infrared camera and the LiDAR sensor. To verify the performance, experiments were conducted in day and night environments. The proposed sensor system and fusion algorithm show that it can reliably detect and identify objects even in environments with poor visibility, such as day or night.

Ji Dong Choi

Min Young Kim

A sensor fusion system with thermal infrared camera and LiDAR for autonomous vehicles and deep learning based object detection

Network Slicing (NS) was proposed as a viable solution in Release 15 of Third Generation Partnership Project (3GPP) to allocate the limited resources among different service types for improving their Quality-of-Service (QoS). However, the advanced vehicular applications such as autonomous driving, platooning, remote driving, etc. have stringent QoS demands and the standard NS architecture is not sustainable for these services. Therefore, we propose a solution compatible with the standard 3GPP NS architecture that implements an Actor-Critic based Deep Reinforcement Learning (DRL) algorithm in the Network Slice Subnet Management Function (NSSMF). The algorithm allocates and manages the limited resources among different slices based on their real-time traffic demands. We generate real-time traffic for each service type and train the algorithm to improve the QoS of each service type in the network. The proposed method is evaluated for the training performance of the proposed algorithm as well as the Service level agreement Satisfaction Ratio (SSR) of each slice. The results exhibit that the proposed method not only improves SSR of each slice, but also performs well in case of increased node density in the network.

Muhammad Ashar Tairq

Malik Muhammad Saad

Muhammad Toaha Raza Khan

Junho Seo

Dongkyun Kim

DRL-based Resource Management in Network Slicing for Vehicular Applications

基于深度强化学习的网络切片资源管理在车载应用中的研究

This study introduces an advanced artificial model, grey wolf optimization (GWO)-nested complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-convolutional neural network (CNN)-bidirectional long short-term memory (BiLSTM), for wind speed forecasting. Initially, CEEMDAN with two nested layers decomposes the time series into intrinsic mode functions (IMFs) to enhance accuracy. Subsequently, CNN extracts features from IMFs, and BiLSTM captures temporal dependencies for precise predictions. GWO further enhances the model by selecting optimal hyperparameters based on decomposition results. Test results on diverse wind speed datasets demonstrate the model’s superiority, with a mean absolute percentage error (MAPE) of approximately 3%.

Quoc Bao Phan

Tuy Tan Nguyen

Enhancing wind speed forecasting accuracy using a GWO-nested CEEMDAN-CNN-BiLSTM model

利用GWO嵌套CEEMDAN-CNN-BiLSTM模型提高风速预测精度

Lu Li

Yong Chen

Meng Li

Yuezhi Liu

Adaptive robust FDI attack detection for cyber–physical​ systems with disturbance

网络物理系统中具有干扰的自适应鲁棒FDI攻击检测

Heejung Yu

Sooyeob Jung

Joon Gyu Ryu

Junse Lee

Sky visibility analysis under urban networks: A stochastic geometry approach

城市网络下的天空可视性分析：一种随机几何方法

Beomju Shin

Taehun Kim

Taikjin Lee

Towards Accurate Positioning: Directionality-Enhanced Fingerprinting Radio Map

面向精确位置定位：方向性增强的指纹无线电地图

Sungmin Lee

Kichang Lee

JaeYeon Park

JeongGil Ko

GMT: Gzip-based Memory-efficient Time-series classification

GMT：基于Gzip的高效内存时序分类

Afsana Kabir Sinthia

Nosin Ibna Mahbub

Eui-Nam Huh

Optimizing proactive content caching with mobility aware deep reinforcement &amp; asynchronous federate learning in VEC

优化VEC中具有移动性感知的深度强化与异步联邦学习的主动内容缓存

Konstantinos Nikiforidis

Alkiviadis Kyrtsoglou

Thanasis Vafeiadis

Thanasis Kotsiopoulos

Alexandros Nizamis

Dimosthenis Ioannidis

Konstantinos Votis

Dimitrios Tzovaras

Panagiotis Sarigiannidis

Enhancing transparency and trust in AI-powered manufacturing: A survey of explainable AI (XAI) applications in smart manufacturing in the era of industry 4.0/5.0

增强人工智能驱动制造中的透明度和信任：工业4.0/5.0时代智能制造中可解释人工智能（XAI）应用的调查

Abbreviation	ICT Express
Journal Impact	4.19
Quartiles(Global)	TELECOMMUNICATIONS(Q2)

ISSN	2405-9595
h-index	44

Publication Information	Publisher: Korean Institute of Communications and Information Sciences，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2015，Proportion of original research papers: 98.00%，Self Citation Rate:7.30%， Gold OA Rate: 95.69%
Journal Fees	$1500Details
Average review cycle	网友分享经验：35 Weeks

ICT Express

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A sensor fusion system with thermal infrared camera and LiDAR for autonomous vehicles and deep learning based object detection

DRL-based Resource Management in Network Slicing for Vehicular Applications

Enhancing wind speed forecasting accuracy using a GWO-nested CEEMDAN-CNN-BiLSTM model

Adaptive robust FDI attack detection for cyber–physical systems with disturbance

Sky visibility analysis under urban networks: A stochastic geometry approach

Sky visibility analysis under urban networks: A stochastic geometry approach

Towards Accurate Positioning: Directionality-Enhanced Fingerprinting Radio Map

GMT: Gzip-based Memory-efficient Time-series classification

Optimizing proactive content caching with mobility aware deep reinforcement & asynchronous federate learning in VEC

Enhancing transparency and trust in AI-powered manufacturing: A survey of explainable AI (XAI) applications in smart manufacturing in the era of industry 4.0/5.0

ICT Express

Journal Citation Format

A journal article with 1 author

A journal article with 2 authors

A journal article with 3 authors

A journal article with 5 or more authors

Books Citation Format

Thesis Citation Format

Web sites Citation Format

Patent Citation Format

Staying up late manually editing references? ivySCI automatically matches journals and helps you generate references with a single click.

Share Submission Experience

A sensor fusion system with thermal infrared camera and LiDAR for autonomous vehicles and deep learning based object detection

DRL-based Resource Management in Network Slicing for Vehicular Applications

Enhancing wind speed forecasting accuracy using a GWO-nested CEEMDAN-CNN-BiLSTM model

Adaptive robust FDI attack detection for cyber–physical​ systems with disturbance

Sky visibility analysis under urban networks: A stochastic geometry approach

Sky visibility analysis under urban networks: A stochastic geometry approach

Towards Accurate Positioning: Directionality-Enhanced Fingerprinting Radio Map

GMT: Gzip-based Memory-efficient Time-series classification

Optimizing proactive content caching with mobility aware deep reinforcement & asynchronous federate learning in VEC

Enhancing transparency and trust in AI-powered manufacturing: A survey of explainable AI (XAI) applications in smart manufacturing in the era of industry 4.0/5.0

Adaptive robust FDI attack detection for cyber–physical systems with disturbance